Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes
Abstract Background Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis‐related genes (DRGs) in AD...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Immunity, Inflammation and Disease |
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| Online Access: | https://doi.org/10.1002/iid3.1037 |
| _version_ | 1797641941482995712 |
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| author | Yidong Zhu Lingyue Kong Tianxiong Han Qiongzhi Yan Jun Liu |
| author_facet | Yidong Zhu Lingyue Kong Tianxiong Han Qiongzhi Yan Jun Liu |
| author_sort | Yidong Zhu |
| collection | DOAJ |
| description | Abstract Background Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis‐related genes (DRGs) in AD remain unknown. Methods Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis‐related molecular clusters. Weighted gene co‐expression network analysis was performed to select hub genes specific to disulfidptosis‐related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets. Results Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD. Conclusion We identified disulfidptosis‐related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder. |
| first_indexed | 2024-03-11T13:52:58Z |
| format | Article |
| id | doaj.art-1cf97ae2ae90437eb118aa18e57d9cea |
| institution | Directory Open Access Journal |
| issn | 2050-4527 |
| language | English |
| last_indexed | 2024-03-11T13:52:58Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Immunity, Inflammation and Disease |
| spelling | doaj.art-1cf97ae2ae90437eb118aa18e57d9cea2023-11-02T07:56:18ZengWileyImmunity, Inflammation and Disease2050-45272023-10-011110n/an/a10.1002/iid3.1037Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypesYidong Zhu0Lingyue Kong1Tianxiong Han2Qiongzhi Yan3Jun Liu4Department of Traditional Chinese Medicine, Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai ChinaDepartment of Traditional Chinese Medicine, Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai ChinaDepartment of Traditional Chinese Medicine, Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai ChinaDepartment of Traditional Chinese Medicine, Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai ChinaDepartment of Traditional Chinese Medicine, Shanghai Tenth People's Hospital Tongji University School of Medicine Shanghai ChinaAbstract Background Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis‐related genes (DRGs) in AD remain unknown. Methods Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis‐related molecular clusters. Weighted gene co‐expression network analysis was performed to select hub genes specific to disulfidptosis‐related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets. Results Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD. Conclusion We identified disulfidptosis‐related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.https://doi.org/10.1002/iid3.1037Alzheimer's diseasedisulfidptosisgene modelimmunitymachine learningmolecular subtypes |
| spellingShingle | Yidong Zhu Lingyue Kong Tianxiong Han Qiongzhi Yan Jun Liu Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes Immunity, Inflammation and Disease Alzheimer's disease disulfidptosis gene model immunity machine learning molecular subtypes |
| title | Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes |
| title_full | Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes |
| title_fullStr | Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes |
| title_full_unstemmed | Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes |
| title_short | Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes |
| title_sort | machine learning identification and immune infiltration of disulfidptosis related alzheimer s disease molecular subtypes |
| topic | Alzheimer's disease disulfidptosis gene model immunity machine learning molecular subtypes |
| url | https://doi.org/10.1002/iid3.1037 |
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